Conference Paper

Large-scale terrain modeling from multiple sensors with dependent Gaussian processes

Australian Centre for Field Robot., Univ. of Sydney, Sydney, NSW, Australia
DOI: 10.1109/IROS.2010.5650769 Conference: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Source: IEEE Xplore

ABSTRACT Terrain modeling remains a challenging yet key component for the deployment of ground robots to the field. The difficulty arrives from the variability of terrain shapes, sparseness of the data, and high degree uncertainty often encountered in large, unstructured environments. This paper presents significant advances to data fusion for stochastic processes modeling spatial data, demonstrated in large-scale terrain modeling tasks. We explore dependent Gaussian processes to provide a multi-resolution representation of space and associated uncertainties, while integrating sensors from different modalities. Experiments performed on multiple multi-modal datasets (3D laser scans and GPS) demonstrate the approach for terrains of about 5 km2.

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